hill climbing algorithm in AI

1. ‫الرحيم‬ ‫الرحمن‬ ‫هللا‬ ‫بسم‬
‫كرري‬ ‫جامعة‬
–
‫الهندسة‬ ‫كلية‬
‫والحاسوب‬ ‫اإللكترونية‬ ‫النظم‬ ‫هندسة‬

‫بعنوان‬ ‫بحث‬
:

‫اعداد‬
:
.1
‫إشراف‬ ‫أحمد‬ ‫مناسك‬
:
.2
‫م‬ ‫جعفر‬ ‫عبدالواحد‬
.
‫د‬
.
‫هللا‬ ‫وهب‬ ‫عثمان‬
.3
‫نادر‬ ‫حمزة‬
Hill Climbing Algorithm In AI

3. 
‫المحتويات‬
:
•
‫خوارزمية‬ ‫هي‬ ‫ما‬
Hill Climbing Algorithm
•
‫ميزات‬
Hill Climbing Algorithm
•
‫الحالة‬ ‫مخطط‬
‫والفضاء‬
State and Space diagram
•
‫أنواع‬
Hill Climbing Algorithm
•
‫مشاكل‬
Hill Climbing Algorithm
•
‫استخدامات‬
Hill Climbing Algorithm

4. Hill Climbing Algorithm :
• Hill Climbing is a heuristic search used for mathematical optimization problems in the field of
Artificial Intelligence.
• Given a large set of inputs and a good heuristic function, it tries to find a sufficiently good solution
to the problem. This solution may not be the global optimal maximum.
• In the above definition, mathematical optimization problems imply that hill-climbing solves the
problems where we need to maximize or minimize a given real function by choosing values from
the given inputs. Example-Travelling salesman problem where we need to minimize the distance
traveled by the salesman.
• ‘Heuristic search’ means that this search algorithm may not find the optimal solution to the
problem. However, it will give a good solution in a reasonable time.
• A heuristic function is a function that will rank all the possible alternatives at any branching step
in the search algorithm based on the available information. It helps the algorithm to select the best
route out of possible routes.

5. •
Hill Climbing
‫الذكا‬ ‫مجال‬ ‫في‬ ‫الرياضي‬ ‫التحسين‬ ‫لمشاكل‬ ‫يستخدم‬ ‫إرشادي‬ ‫بحث‬ ‫هو‬
‫ء‬
‫االصطناعي‬
.
•
‫ح‬ ‫إيجاد‬ ‫يحاول‬ ‫فإنه‬ ، ‫جيدة‬ ‫إرشادية‬ ‫ووظيفة‬ ‫المدخالت‬ ‫من‬ ‫كبيرة‬ ‫مجموعة‬ ‫إلى‬ ‫بالنظر‬
‫جيد‬ ‫ل‬
‫للمشكلة‬ ‫الكفاية‬ ‫فيه‬ ‫بما‬
.
‫األمثل‬ ‫العالمي‬ ‫األقصى‬ ‫الحد‬ ‫هو‬ ‫الحل‬ ‫هذا‬ ‫يكون‬ ‫ال‬ ‫قد‬
.
•
‫يح‬ ‫التل‬ ‫تسلق‬ ‫أن‬ ‫إلى‬ ‫الرياضي‬ ‫التحسين‬ ‫مشاكل‬ ‫تشير‬ ، ‫أعاله‬ ‫التعريف‬ ‫في‬
‫المشكالت‬ ‫ل‬
‫اختيار‬ ‫طريق‬ ‫عن‬ ‫معينة‬ ‫حقيقية‬ ‫وظيفة‬ ‫تقليل‬ ‫أو‬ ‫تعظيم‬ ‫إلى‬ ‫فيها‬ ‫نحتاج‬ ‫التي‬
‫من‬ ‫القيم‬
‫المعطاة‬ ‫المدخالت‬
.
‫المسافة‬ ‫تقليل‬ ‫إلى‬ ‫نحتاج‬ ‫حيث‬ ‫السفر‬ ‫بائع‬ ‫مشكلة‬ ‫على‬ ‫مثال‬
‫قطعها‬ ‫التي‬
‫البائع‬
.
•
‫يعني‬
"
‫اإلرشادي‬ ‫البحث‬
"
‫للمشكل‬ ‫األمثل‬ ‫الحل‬ ‫تجد‬ ‫ال‬ ‫قد‬ ‫هذه‬ ‫البحث‬ ‫خوارزمية‬ ‫أن‬
‫ة‬
.
‫ومع‬
‫معقول‬ ‫وقت‬ ‫في‬ ‫ًا‬‫د‬‫جي‬ ً‫ال‬‫ح‬ ‫سيعطي‬ ‫فإنه‬ ، ‫ذلك‬
.
‫أ‬ ‫في‬ ‫الممكنة‬ ‫البدائل‬ ‫جميع‬ ‫ترتيب‬ ‫شأنها‬ ‫من‬ ‫وظيفة‬ ‫هي‬ ‫االستكشافية‬ ‫الوظيفة‬
‫خطوة‬ ‫ي‬
‫المتاحة‬ ‫المعلومات‬ ‫على‬ ً‫ء‬‫بنا‬ ‫البحث‬ ‫خوارزمية‬ ‫في‬ ‫متفرعة‬
.
‫ت‬ ‫في‬ ‫الخوارزمية‬ ‫يساعد‬
‫حديد‬
‫الممكنة‬ ‫المسارات‬ ‫من‬ ‫للخروج‬ ‫طريق‬ ‫أفضل‬
.

6. • This algorithm has a node that comprises two parts: state and value.
It begins with a non-optimal state (the hill’s base) and upgrades this
state until a certain precondition is met. The heuristic function is used
as the basis for this precondition. The process of continuous
improvement of the current state of iteration can be termed as
climbing. This explains why the algorithm is termed as a hill-climbing
algorithm.
• A hill-climbing algorithm’s objective is to attain an optimal state that
is an upgrade of the existing state. When the current state is
improved, the algorithm will perform further incremental changes to
the improved state. This process will continue until a peak solution is
achieved. The peak state cannot undergo further improvements.

7. ‫تحتوي‬
‫جزأين‬ ‫من‬ ‫تتكون‬ ‫عقدة‬ ‫على‬ ‫الخوارزمية‬ ‫هذه‬
:
‫والقيمة‬ ‫الحالة‬
.
‫غير‬ ‫بحالة‬ ‫تبدأ‬
‫مثالية‬
(
‫التل‬ ‫قاعدة‬
)
‫معين‬ ‫مسبق‬ ‫شرط‬ ‫استيفاء‬ ‫يتم‬ ‫حتى‬ ‫الحالة‬ ‫هذه‬ ‫وترقي‬
.
‫استخدام‬ ‫يتم‬
‫الوظيفة‬
‫المسبق‬ ‫الشرط‬ ‫لهذا‬ ‫كأساس‬ ‫األمور‬ ‫مجريات‬ ‫عن‬ ‫الكشف‬
.
‫التحسين‬ ‫عملية‬ ‫وصف‬ ‫يمكن‬
‫بالتسلق‬ ‫للتكرار‬ ‫الحالية‬ ‫للحالة‬ ‫المستمر‬
.
‫الخوارزمي‬ ‫تسمية‬ ‫سبب‬ ‫يفسر‬ ‫ما‬ ‫وهذا‬
‫بخوارزمية‬ ‫ة‬
‫التل‬ ‫تسلق‬
.
‫هدف‬
‫للوضع‬ ‫ترقية‬ ‫هي‬ ‫التي‬ ‫المثلى‬ ‫الحالة‬ ‫إلى‬ ‫الوصول‬ ‫هو‬ ‫التالل‬ ‫تسلق‬ ‫خوارزمية‬
‫الحالي‬
.
‫التغييرات‬ ‫من‬ ‫المزيد‬ ‫بإجراء‬ ‫الخوارزمية‬ ‫ستقوم‬ ، ‫الحالية‬ ‫الحالة‬ ‫تحسين‬ ‫يتم‬ ‫عندما‬
‫اإلضافية‬
‫المحسنة‬ ‫الحالة‬ ‫على‬
.
‫الذروة‬ ‫حل‬ ‫إلى‬ ‫الوصول‬ ‫يتم‬ ‫حتى‬ ‫العملية‬ ‫هذه‬ ‫ستستمر‬
.
‫يمك‬ ‫ال‬
‫أن‬ ‫ن‬
‫التحسينات‬ ‫من‬ ‫لمزيد‬ ‫الذروة‬ ‫حالة‬ ‫تخضع‬
.

8. Applications of hill climbing algorithm
The hill-climbing algorithm can be applied in the following areas:
• Marketing
A hill-climbing algorithm can help a marketing manager to develop the best marketing plans. This algorithm is
widely used in solving Traveling-Salesman problems. It can help by optimizing the distance covered and
improving the travel time of sales team members. The algorithm helps establish the local minima efficiently.
• Robotics
Hill climbing is useful in the effective operation of robotics. It enhances the coordination of different systems
and components in robots.
• Job Scheduling
The hill climbing algorithm has also been applied in job scheduling. This is a process in which system resources
are allocated to different tasks within a computer system. Job scheduling is achieved through the migration of
jobs from one node to a neighboring node. A hill-climbing technique helps establish the right migration route

9. Hill Climbing algorithm Features
1. Variant of generating and test algorithm:
It is a variant of generating and testing algorithms. The generate and test algorithm is as
follows :
• Generate possible solutions.
• Test to see if this is the expected solution.
• If the solution has been found quit else go to step 1.
• Hence we call Hill climbing a variant of generating and test algorithm as it takes the
feedback from the test procedure. Then this feedback is utilized by the generator in
deciding the next move in the search space.
2. Uses the Greedy approach:
• At any point in state space, the search moves in that direction only which optimizes the
cost of function with the hope of finding the optimal solution at the end.

10. •
1
.
‫الخوارزمية‬ ‫واختبار‬ ‫توليد‬ ‫متغير‬
:
•
‫الخوارزميات‬ ‫واختبار‬ ‫إنشاء‬ ‫من‬ ‫نوع‬ ‫إنه‬
.
‫يلي‬ ‫كما‬ ‫واالختبار‬ ‫اإلنشاء‬ ‫خوارزمية‬ ‫تكون‬
:
•
‫الممكنة‬ ‫الحلول‬ ‫استخرج‬
.
•
‫المتوقع‬ ‫الحل‬ ‫هو‬ ‫هذا‬ ‫كان‬ ‫إذا‬ ‫ما‬ ‫لمعرفة‬ ‫اختبر‬
.
•
‫الخطوة‬ ‫إلى‬ ‫االنتقال‬ ‫بإنهاء‬ ‫فقم‬ ، ‫الحل‬ ‫على‬ ‫العثور‬ ‫تم‬ ‫إذا‬
1
.
•
‫على‬ ‫نطلق‬ ‫فإننا‬ ‫ثم‬ ‫ومن‬
Hill
‫تأخذ‬ ‫ألنها‬ ‫واالختبار‬ ‫التوليد‬ ‫خوارزمية‬ ‫من‬ ‫متغير‬ ‫تسلق‬
‫االختبار‬ ‫إجراء‬ ‫من‬ ‫التعليقات‬
.
‫في‬ ‫المولد‬ ‫قبل‬ ‫من‬ ‫التعليقات‬ ‫هذه‬ ‫استخدام‬ ‫يتم‬ ‫ثم‬
‫الخطوة‬ ‫تحديد‬
‫البحث‬ ‫مساحة‬ ‫في‬ ‫التالية‬
.
•
2
.
‫الجشع‬ ‫النهج‬ ‫يستخدم‬
:
•
‫ت‬ ‫إلى‬ ‫يؤدي‬ ‫مما‬ ‫فقط‬ ‫االتجاه‬ ‫هذا‬ ‫في‬ ‫البحث‬ ‫يتحرك‬ ، ‫الدولة‬ ‫مساحة‬ ‫في‬ ‫نقطة‬ ‫أي‬ ‫في‬
‫حسين‬
‫النهاية‬ ‫في‬ ‫األمثل‬ ‫الحل‬ ‫إيجاد‬ ‫أمل‬ ‫على‬ ‫الوظيفة‬ ‫تكلفة‬
.

11. State Space diagram for Hill Climbing
• The state-space diagram is a graphical representation of the set of
states our search algorithm can reach vs the value of our objective
function(the function which we wish to maximize).
• X-axis: denotes the state space ie states or configuration our
algorithm may reach.
• Y-axis: denotes the values of objective function corresponding to a
particular state.
• The best solution will be a state space where the objective function
has a maximum value(global maximum).

12. •
‫إلي‬ ‫تصل‬ ‫أن‬ ‫يمكن‬ ‫التي‬ ‫الحاالت‬ ‫لمجموعة‬ ‫رسومي‬ ‫تمثيل‬ ‫هو‬ ‫والفضاء‬ ‫الدولة‬ ‫مخطط‬
‫ها‬
‫الموضوعية‬ ‫وظيفتنا‬ ‫قيمة‬ ‫مقابل‬ ‫لدينا‬ ‫البحث‬ ‫خوارزمية‬
(
‫ف‬ ‫نرغب‬ ‫التي‬ ‫الوظيفة‬
‫تعظيمها‬ ‫ي‬
.)
•
‫السيني‬ ‫المحور‬
:
‫تصل‬ ‫قد‬ ‫الذي‬ ‫التكوين‬ ‫أو‬ ‫الحاالت‬ ‫أي‬ ، ‫الحالة‬ ‫مساحة‬ ‫إلى‬ ‫يشير‬
‫إليه‬
‫الخوارزمية‬
.
•
‫ص‬ ‫المحور‬
:
‫معينة‬ ‫لحالة‬ ‫المقابلة‬ ‫الموضوعية‬ ‫الوظيفة‬ ‫قيم‬ ‫إلى‬ ‫يشير‬
.
•
‫قصوى‬ ‫قيمة‬ ‫الهدف‬ ‫للوظيفة‬ ‫يكون‬ ‫حيث‬ ‫الحالة‬ ‫مساحة‬ ‫هو‬ ‫حل‬ ‫أفضل‬ ‫سيكون‬
(
‫ا‬ ‫الحد‬
‫ألقصى‬
‫العالمي‬
.)

13. •Local maximum: It is a state which is better than its
neighboring state however there exists a state which is
better than it(global maximum). This state is better
because here the value of the objective function is higher
than its neighbors.
•Global maximum: It is the best possible state in the
state space diagram. This is because, at this stage, the
objective function has the highest value.
•Plateau/flat local maximum: It is a flat region of state
space where neighboring states have the same value.
•Ridge: It is a region that is higher than its neighbors but
itself has a slope. It is a special kind of local maximum.
•Current state: The region of the state space diagram
where we are currently present during the search.
•Shoulder: It is a plateau that has an uphill edge

14. •
‫المحلي‬ ‫األقصى‬ ‫الحد‬
:
‫أفضل‬ ‫دولة‬ ‫توجد‬ ‫ولكن‬ ‫لها‬ ‫المجاورة‬ ‫الدولة‬ ‫من‬ ‫أفضل‬ ‫دولة‬ ‫وهي‬
‫منها‬
(
‫العالمي‬ ‫األقصى‬ ‫الحد‬
.)
‫أعل‬ ‫هنا‬ ‫الموضوعية‬ ‫الوظيفة‬ ‫قيمة‬ ‫ألن‬ ‫أفضل‬ ‫الحالة‬ ‫هذه‬
‫من‬ ‫ى‬
‫جيرانها‬
.
•
‫العالمي‬ ‫األقصى‬ ‫الحد‬
:
‫الوالية‬ ‫مساحة‬ ‫مخطط‬ ‫في‬ ‫ممكنة‬ ‫حالة‬ ‫أفضل‬ ‫هو‬
.
‫ه‬ ‫في‬ ، ‫ألنه‬ ‫هذا‬
‫ذه‬
‫قيمة‬ ‫أعلى‬ ‫لها‬ ‫الهدف‬ ‫الوظيفة‬ ، ‫المرحلة‬
.
•
‫الهضبة‬
/
‫المسطح‬ ‫المحلي‬ ‫األقصى‬ ‫الحد‬
:
‫لل‬ ‫حيث‬ ‫الدولة‬ ‫مساحة‬ ‫من‬ ‫مسطحة‬ ‫منطقة‬ ‫إنها‬
‫دول‬
‫القيمة‬ ‫نفس‬ ‫المجاورة‬
.
•
‫ريدج‬
:
‫منحدر‬ ‫لها‬ ‫ولكن‬ ‫جيرانها‬ ‫من‬ ‫أعلى‬ ‫منطقة‬ ‫إنها‬
.
‫األقصى‬ ‫الحد‬ ‫من‬ ‫خاص‬ ‫نوع‬ ‫إنه‬
‫المحلي‬
.
•
‫الحالية‬ ‫الحالة‬
:
‫البحث‬ ‫أثناء‬ ‫ا‬ً‫ي‬‫حال‬ ‫نتواجد‬ ‫حيث‬ ‫الوالية‬ ‫مساحة‬ ‫مخطط‬ ‫منطقة‬
.
•
‫الكتف‬
:
‫شاقة‬ ‫حافة‬ ‫لها‬ ‫هضبة‬ ‫إنها‬

15. Types of Hill Climbing
1. Simple Hill climbing:
It examines the neighboring nodes one by one and selects the first neighboring node which
optimizes the current cost as the next node.
Algorithm for Simple Hill climbing :
• Evaluate the initial state. If it is a goal state then stop and return success. Otherwise, make the
initial state as the current state.
• Loop until the solution state is found or there are no new operators present which can be
applied to the current state.
• Select a state that has not been yet applied to the current state and apply it to produce a new
state.
• Perform these to evaluate the new state.
• If the current state is a goal state, then stop and return success.
• If it is better than the current state, then make it the current state and proceed further.
• If it is not better than the current state, then continue in the loop until a solution is found.
• Exit from the function.

16. 1
.
‫البسيط‬ ‫التل‬ ‫تسلق‬
:
‫تحسي‬ ‫على‬ ‫تعمل‬ ‫التي‬ ‫األولى‬ ‫المجاورة‬ ‫العقدة‬ ‫ويختار‬ ‫األخرى‬ ‫تلو‬ ‫واحدة‬ ‫المجاورة‬ ‫العقد‬ ‫يفحص‬
‫التكلفة‬ ‫ن‬
‫تالية‬ ‫كعقدة‬ ‫الحالية‬
.
‫البسيطة‬ ‫التالل‬ ‫لتسلق‬ ‫خوارزمية‬
:
‫األولية‬ ‫الحالة‬ ‫تقييم‬
.
‫النجاح‬ ‫وأعد‬ ‫فتوقف‬ ، ‫الهدف‬ ‫حالة‬ ‫كانت‬ ‫إذا‬
.
‫األولي‬ ‫الحالة‬ ‫اجعل‬ ، ‫ذلك‬ ‫خالف‬
‫كالحالة‬ ‫ة‬
‫الحالية‬
.
‫تطبيقها‬ ‫يمكن‬ ‫جديدة‬ ‫تشغيل‬ ‫عوامل‬ ‫توجد‬ ‫ال‬ ‫أو‬ ‫الحل‬ ‫حالة‬ ‫على‬ ‫العثور‬ ‫يتم‬ ‫حتى‬ ‫بالتكرار‬ ‫قم‬
‫الحالة‬ ‫على‬
‫الحالية‬
.
‫جديدة‬ ‫حالة‬ ‫إلنتاج‬ ‫بتطبيقها‬ ‫وقم‬ ‫الحالية‬ ‫الحالة‬ ‫على‬ ‫بعد‬ ‫تطبيقها‬ ‫يتم‬ ‫لم‬ ‫حالة‬ ‫حدد‬
.
‫الجديدة‬ ‫الحالة‬ ‫لتقييم‬ ‫هذه‬ ‫بإجراء‬ ‫قم‬
.
‫النجاح‬ ‫وأعد‬ ‫فتوقف‬ ، ‫الهدف‬ ‫حالة‬ ‫هي‬ ‫الحالية‬ ‫الحالة‬ ‫كانت‬ ‫إذا‬
.
‫ا‬ً‫م‬‫قد‬ ِ
‫وامض‬ ‫الحالية‬ ‫الحالة‬ ‫فاجعلها‬ ، ‫الحالية‬ ‫الحالة‬ ‫من‬ ‫أفضل‬ ‫كانت‬ ‫إذا‬
.
‫حل‬ ‫على‬ ‫العثور‬ ‫يتم‬ ‫حتى‬ ‫الحلقة‬ ‫في‬ ‫فاستمر‬ ، ‫الحالية‬ ‫الحالة‬ ‫من‬ ‫أفضل‬ ‫يكن‬ ‫لم‬ ‫إذا‬
.
‫الوظيفة‬ ‫من‬ ‫الخروج‬
.

17. 2. Steepest-Ascent Hill climbing:
It first examines all the neighboring nodes and then selects the node closest to the solution state
as of the next node.
Algorithm for Steepest Ascent Hill climbing :
• Evaluate the initial state. If it is a goal state then stop and return success. Otherwise, make the
initial state as the current state.
• Repeat these steps until a solution is found or the current state does not change
• Select a state that has not been yet applied to the current state.
• Initialize a new ‘best state’ equal to the current state and apply it to produce a new state.
• Perform these to evaluate the new state
• If the current state is a goal state, then stop and return success.
• If it is better than the best state, then make it the best state else continue the loop with
another new state.
• Make the best state as the current state and go to Step 2 of the second point.
• Exit from the function.

18. 2
.
‫االنحدار‬ ‫شديدة‬ ‫التالل‬ ‫تسلق‬
:
‫ال‬ ‫من‬ ‫ا‬ً‫اعتبار‬ ‫الحل‬ ‫حالة‬ ‫إلى‬ ‫األقرب‬ ‫العقدة‬ ‫يختار‬ ‫ثم‬ ‫المجاورة‬ ‫العقد‬ ‫جميع‬ ‫بفحص‬ ً‫ال‬‫أو‬ ‫يقوم‬
‫التالية‬ ‫عقدة‬
.
‫االنحدار‬ ‫الشديدة‬ ‫المنحدرات‬ ‫لتسلق‬ ‫خوارزمية‬
:
‫األولية‬ ‫الحالة‬ ‫تقييم‬
.
‫النجاح‬ ‫وأعد‬ ‫فتوقف‬ ، ‫الهدف‬ ‫حالة‬ ‫كانت‬ ‫إذا‬
.
‫األولي‬ ‫الحالة‬ ‫اجعل‬ ، ‫ذلك‬ ‫خالف‬
‫كالحالة‬ ‫ة‬
‫الحالية‬
.
‫الحالية‬ ‫الحالة‬ ‫تتغير‬ ‫ال‬ ‫أو‬ ‫حل‬ ‫على‬ ‫العثور‬ ‫يتم‬ ‫حتى‬ ‫الخطوات‬ ‫هذه‬ ‫كرر‬
‫الحالية‬ ‫الحالة‬ ‫على‬ ‫بعد‬ ‫تطبيقها‬ ‫يتم‬ ‫لم‬ ‫حالة‬ ‫حدد‬
.
‫بدء‬
"
‫حالة‬ ‫أفضل‬
"
‫جديدة‬ ‫حالة‬ ‫إلنتاج‬ ‫وتطبيقها‬ ‫الحالية‬ ‫للحالة‬ ‫مساوية‬ ‫جديدة‬
.
‫الجديدة‬ ‫الحالة‬ ‫لتقييم‬ ‫هذه‬ ‫بإجراء‬ ‫قم‬
‫النجاح‬ ‫وأعد‬ ‫فتوقف‬ ، ‫الهدف‬ ‫حالة‬ ‫هي‬ ‫الحالية‬ ‫الحالة‬ ‫كانت‬ ‫إذا‬
.
‫أخرى‬ ‫جديدة‬ ‫حالة‬ ‫مع‬ ‫الحلقة‬ ‫تابع‬ ‫ثم‬ ، ‫حالة‬ ‫أفضل‬ ‫فاجعلها‬ ، ‫حالة‬ ‫أفضل‬ ‫من‬ ‫أفضل‬ ‫كانت‬ ‫إذا‬
.
‫الخطوة‬ ‫إلى‬ ‫وانتقل‬ ‫الحالية‬ ‫كالحالة‬ ‫حالة‬ ‫أفضل‬ ‫اجعل‬
2
‫الثانية‬ ‫النقطة‬ ‫من‬
.
‫الوظيفة‬ ‫من‬ ‫الخروج‬
.

19. 3. Stochastic hill climbing:
• It does not examine all the neighboring nodes before deciding which node to
select. It just selects a neighboring node at random and decides (based on the
amount of improvement in that neighbor) whether to move to that neighbor or
to examine another.
• Evaluate the initial state. If it is a goal state then stop and return success.
Otherwise, make the initial state the current state.
• Repeat these steps until a solution is found or the current state does not
change.
• Select a state that has not been yet applied to the current state.
• Apply the successor function to the current state and generate all the
neighbor states.
• Among the generated neighbor states which are better than the current state
choose a state randomly (or based on some probability function).
• If the chosen state is the goal state, then return success, else make it the
current state and repeat step 2 of the second point.
• Exit from the function.

20. 3
.
‫العشوائي‬ ‫التل‬ ‫تسلق‬
:
•
‫تحديدها‬ ‫سيتم‬ ‫التي‬ ‫العقدة‬ ‫تحديد‬ ‫قبل‬ ‫المجاورة‬ ‫العقد‬ ‫جميع‬ ‫بفحص‬ ‫يقوم‬ ‫ال‬
.
‫فق‬ ‫إنه‬
‫عقدة‬ ‫يختار‬ ‫ط‬
‫ويقرر‬ ‫عشوائي‬ ‫بشكل‬ ‫مجاورة‬
(
‫الجار‬ ‫ذلك‬ ‫في‬ ‫التحسين‬ ‫مقدار‬ ‫على‬ ً‫ء‬‫بنا‬
)
‫سيت‬ ‫كان‬ ‫إذا‬ ‫ما‬
‫االنتقال‬ ‫م‬
‫آخر‬ ‫فحص‬ ‫أو‬ ‫الجار‬ ‫هذا‬ ‫إلى‬
.
•
‫األولية‬ ‫الحالة‬ ‫تقييم‬
.
‫النجاح‬ ‫وأعد‬ ‫فتوقف‬ ، ‫الهدف‬ ‫حالة‬ ‫كانت‬ ‫إذا‬
.
‫الح‬ ‫اجعل‬ ، ‫ذلك‬ ‫خالف‬
‫الة‬
‫الحالية‬ ‫الحالة‬ ‫هي‬ ‫األولية‬
.
•
‫الحالية‬ ‫الحالة‬ ‫تتغير‬ ‫ال‬ ‫أو‬ ‫حل‬ ‫على‬ ‫العثور‬ ‫يتم‬ ‫حتى‬ ‫الخطوات‬ ‫هذه‬ ‫كرر‬
.
•
‫الحالية‬ ‫الحالة‬ ‫على‬ ‫بعد‬ ‫تطبيقها‬ ‫يتم‬ ‫لم‬ ‫حالة‬ ‫حدد‬
.
•
‫المجاورة‬ ‫الدول‬ ‫جميع‬ ‫وإنشاء‬ ‫الحالية‬ ‫الحالة‬ ‫على‬ ‫الالحقة‬ ‫الوظيفة‬ ‫تطبيق‬
.
•
‫اخت‬ ، ‫الحالية‬ ‫الحالة‬ ‫من‬ ‫أفضل‬ ‫تكون‬ ‫والتي‬ ‫إنشاؤها‬ ‫تم‬ ‫التي‬ ‫المجاورة‬ ‫الدول‬ ‫بين‬ ‫من‬
‫حالة‬ ‫ر‬
‫عشوائي‬ ‫بشكل‬
(
‫االحتمال‬ ‫وظائف‬ ‫بعض‬ ‫على‬ ً‫ء‬‫بنا‬ ‫أو‬
.)
•
‫الحال‬ ‫الحالة‬ ‫اجعلها‬ ‫وإال‬ ، ‫النجاح‬ ‫بإرجاع‬ ‫فقم‬ ، ‫الهدف‬ ‫حالة‬ ‫هي‬ ‫المختارة‬ ‫الحالة‬ ‫كانت‬ ‫إذا‬
‫ية‬
‫الخطوة‬ ‫وكرر‬
2
‫الثانية‬ ‫النقطة‬ ‫من‬
.
•
‫الوظيفة‬ ‫من‬ ‫الخروج‬
.

21. Problems in different regions in Hill climbing
Hill climbing cannot reach the optimal/best state(global maximum) if it enters any of the following regions :
• Local maximum: At a local maximum all neighboring states have a value that is worse than the current
state. Since hill-climbing uses a greedy approach, it will not move to the worse state and terminate itself. The
process will end even though a better solution may exist.
• To overcome the local maximum problem: Utilize the backtracking technique. Maintain a list of visited
states. If the search reaches an undesirable state, it can backtrack to the previous configuration and explore a
new path.
• Plateau: On the plateau, all neighbors have the same value. Hence, it is not possible to select the best
direction.
• To overcome plateaus: Make a big jump. Randomly select a state far away from the current state. Chances
are that we will land in a non-plateau region.
• Ridge: Any point on a ridge can look like a peak because movement in all possible directions is downward.
Hence the algorithm stops when it reaches this state.
• To overcome Ridge: In this kind of obstacle, use two or more rules before testing. It implies moving in
several directions at once.

22. Plateau
• In this region, the values
attained by the neighboring
states are the same. This
makes it difficult for the
algorithm to choose the best
direction.
• This challenge can be
overcome by taking a huge
jump that will lead you to a
non-plateau space.

23. Ridge
• The hill-climbing algorithm
may terminate itself when it
reaches a ridge. This is
because the peak of the ridge
is followed by downward
movement rather than upward
movement.
• This impediment can be solved
by going in different directions
at once.

24. •
‫التالل‬ ‫تسلق‬ ‫في‬ ‫مختلفة‬ ‫مناطق‬ ‫في‬ ‫مشاكل‬
•
‫المثلى‬ ‫الحالة‬ ‫إلى‬ ‫التالل‬ ‫تسلق‬ ‫يصل‬ ‫أن‬ ‫يمكن‬ ‫ال‬
/
‫األفضل‬
(
‫العالمي‬ ‫األقصى‬ ‫الحد‬
)
‫المن‬ ‫من‬ ‫ا‬ً‫ي‬‫أ‬ ‫دخل‬ ‫إذا‬
‫التالية‬ ‫اطق‬
:
•
‫المحلي‬ ‫األقصى‬ ‫الحد‬
:
‫الحال‬ ‫الحالة‬ ‫من‬ ‫أسوأ‬ ‫قيمة‬ ‫المجاورة‬ ‫الدول‬ ‫لجميع‬ ‫يكون‬ ، ‫المحلي‬ ‫األقصى‬ ‫الحد‬ ‫في‬
‫ية‬
.
‫ا‬ً‫نظر‬
‫نفسه‬ ‫وينهي‬ ‫األسوأ‬ ‫الحالة‬ ‫إلى‬ ‫ينتقل‬ ‫فلن‬ ، ‫ا‬ً‫ع‬‫جش‬ ‫ا‬ً‫ج‬‫نه‬ ‫يستخدم‬ ‫التل‬ ‫تسلق‬ ‫ألن‬
.
‫العملي‬ ‫ستنتهي‬
‫وجود‬ ‫من‬ ‫الرغم‬ ‫على‬ ‫ة‬
‫أفضل‬ ‫حل‬
.
•
‫المحلية‬ ‫األقصى‬ ‫الحد‬ ‫مشكلة‬ ‫على‬ ‫للتغلب‬
:
‫التراجع‬ ‫تقنية‬ ‫استخدم‬
.
‫ت‬ ‫التي‬ ‫بالدول‬ ‫بقائمة‬ ‫احتفظ‬
‫زيارتها‬ ‫مت‬
.
‫إذا‬
‫جديد‬ ‫مسار‬ ‫واستكشاف‬ ‫السابق‬ ‫التكوين‬ ‫إلى‬ ‫الرجوع‬ ‫فيمكنه‬ ، ‫فيها‬ ‫مرغوب‬ ‫غير‬ ‫حالة‬ ‫إلى‬ ‫البحث‬ ‫وصل‬
.
•
‫الهضبة‬
:
‫القيمة‬ ‫نفس‬ ‫لها‬ ‫الجيران‬ ‫جميع‬ ، ‫الهضبة‬ ‫في‬
.
‫األفضل‬ ‫االتجاه‬ ‫تحديد‬ ‫يمكن‬ ‫ال‬ ، ‫وبالتالي‬
.
•
‫الهضاب‬ ‫على‬ ‫للتغلب‬
:
‫كبيرة‬ ‫بقفزة‬ ‫قم‬
.
‫الحالية‬ ‫الحالة‬ ‫عن‬ ‫بعيدة‬ ‫حالة‬ ‫عشوائي‬ ‫بشكل‬ ‫حدد‬
.
‫احت‬ ‫هناك‬
‫بأننا‬ ‫ماالت‬
‫هضبة‬ ‫غير‬ ‫منطقة‬ ‫في‬ ‫سنهبط‬
.
•
‫ريدج‬
:
‫الممكن‬ ‫االتجاهات‬ ‫جميع‬ ‫في‬ ‫الحركة‬ ‫ألن‬ ‫قمة‬ ‫وكأنها‬ ‫تبدو‬ ‫أن‬ ‫يمكن‬ ‫التالل‬ ‫من‬ ‫سلسلة‬ ‫على‬ ‫نقطة‬ ‫أي‬
‫تكون‬ ‫ة‬
‫نزولية‬
.
‫الحالة‬ ‫هذه‬ ‫إلى‬ ‫تصل‬ ‫عندما‬ ‫الخوارزمية‬ ‫تتوقف‬ ‫ثم‬ ‫ومن‬
.
•
‫ريدج‬ ‫على‬ ‫للتغلب‬
:
‫االختبار‬ ‫قبل‬ ‫أكثر‬ ‫أو‬ ‫قاعدتين‬ ‫استخدم‬ ، ‫العوائق‬ ‫من‬ ‫النوع‬ ‫هذا‬ ‫في‬
.
‫ال‬ ‫يعني‬ ‫إنه‬
‫عدة‬ ‫في‬ ‫تحرك‬
‫واحد‬ ‫وقت‬ ‫في‬ ‫اتجاهات‬
.

25. Applications of hill climbing algorithm
The hill-climbing algorithm can be applied in the following areas:
• Marketing
A hill-climbing algorithm can help a marketing manager to develop the best marketing plans. This algorithm is
widely used in solving Traveling-Salesman problems. It can help by optimizing the distance covered and
improving the travel time of sales team members. The algorithm helps establish the local minima efficiently.
• Robotics
Hill climbing is useful in the effective operation of robotics. It enhances the coordination of different systems
and components in robots.
• Job Scheduling
The hill climbing algorithm has also been applied in job scheduling. This is a process in which system resources
are allocated to different tasks within a computer system. Job scheduling is achieved through the migration of
jobs from one node to a neighboring node. A hill-climbing technique helps establish the right migration route

26. ‫التل‬ ‫تسلق‬ ‫خوارزمية‬ ‫تطبيقات‬
‫التالية‬ ‫المجاالت‬ ‫في‬ ‫التل‬ ‫تسلق‬ ‫خوارزمية‬ ‫تطبيق‬ ‫يمكن‬
:
‫تسويق‬
‫التسويق‬ ‫خطط‬ ‫أفضل‬ ‫تطوير‬ ‫على‬ ‫التسويق‬ ‫مدير‬ ‫التل‬ ‫تسلق‬ ‫خوارزمية‬ ‫تساعد‬ ‫أن‬ ‫يمكن‬
.
‫واسع‬ ‫نطاق‬ ‫على‬ ‫الخوارزمية‬ ‫هذه‬ ‫تستخدم‬
‫مشاكل‬ ‫حل‬ ‫في‬
‫متجول‬ ‫بائع‬
.
‫المبيعات‬ ‫فريق‬ ‫ألعضاء‬ ‫السفر‬ ‫وقت‬ ‫وتحسين‬ ‫المقطوعة‬ ‫المسافة‬ ‫تحسين‬ ‫خالل‬ ‫من‬ ‫يساعد‬ ‫أن‬ ‫يمكن‬
.
‫الخوارز‬ ‫تساعد‬
‫إنشاء‬ ‫في‬ ‫مية‬
‫بكفاءة‬ ‫المحلية‬ ‫الدنيا‬ ‫الحدود‬
.
‫الروبوتات‬ ‫علم‬
‫للروبوتات‬ ‫الفعال‬ ‫التشغيل‬ ‫في‬ ‫مفيد‬ ‫التالل‬ ‫تسلق‬
.
‫الروبوتات‬ ‫في‬ ‫المختلفة‬ ‫والمكونات‬ ‫األنظمة‬ ‫بين‬ ‫التنسيق‬ ‫يعزز‬
.
‫الوظائف‬ ‫جدولة‬
‫الوظائف‬ ‫جدولة‬ ‫في‬ ‫ا‬ً‫ض‬‫أي‬ ‫التل‬ ‫تسلق‬ ‫خوارزمية‬ ‫تطبيق‬ ‫تم‬
.
‫ا‬ ‫نظام‬ ‫داخل‬ ‫مختلفة‬ ‫لمهام‬ ‫النظام‬ ‫موارد‬ ‫تخصيص‬ ‫فيها‬ ‫يتم‬ ‫عملية‬ ‫هذه‬
‫لكمبيوتر‬
.
‫يتم‬
‫مجاورة‬ ‫عقدة‬ ‫إلى‬ ‫واحدة‬ ‫عقدة‬ ‫من‬ ‫الوظائف‬ ‫ترحيل‬ ‫خالل‬ ‫من‬ ‫الوظائف‬ ‫جدولة‬ ‫تحقيق‬
.
‫الهج‬ ‫طريق‬ ‫تحديد‬ ‫في‬ ‫التل‬ ‫تسلق‬ ‫تقنية‬ ‫تساعد‬
‫الصحيح‬ ‫رة‬

27. Conclusion
• Hill climbing is a very resourceful technique used in solving huge
computational problems. It can help establish the best solution for
problems. This technique has the potential of revolutionizing
optimization within artificial intelligence.
• In the future, technological advancement to the hill climbing
technique will solve diverse and unique optimization problems with
better advanced features